Suggesting Search Results to Users Before Receiving Any Search Query from the Users

ABSTRACT

In one embodiment, a social-networking system may compile a set of search results based on information known about a user stored by the social-networking system, the search results being compiled before the user inputs any search query or portion thereof, each search result being associated with one or more call-to-action elements applicable to the search result, each call-to-action element prompting an action from the user related to the search result via the social-networking system, and send the set of search results with the call-to-action elements for presentation to the user, wherein the call-to-action elements are presented to the user in proximity to their associated search results.

PRIORITY

This application is a continuation under 35 U.S.C. §120 of U.S. patentapplication Ser. No. 13/191,307, filed 26 Jul. 2011, which is acontinuation-in-part under 35 U.S.C. §120 of U.S. patent applicationSer. No. 13/152,664, filed on 3 Jun. 2011, issued as U.S. Pat. No.9,110,992 on 18 Aug. 2015.

TECHNICAL FIELD

This disclosure generally relates to improving search experiences forInternet users.

BACKGROUND

The Internet provides a vast amount of information, which may be storedat many different sites and on many different devices, such as onservers and clients or in databases, around the world. These differentdevices at the different sites are communicatively linked to computer orcommunication networks over wire-line or wireless connections. A personmay access specific pieces of information available on the Internetusing a suitable network device (e.g., a computer, a smart mobiletelephone, an entertainment console, etc.) connected to a network.

Due to the sheer amount of information available on the Internet, it isimpractical as well as impossible for a person (e.g., a network user) tomanually search throughout the Internet for the specific pieces ofinformation he needs. Instead, most network users rely on differenttypes of computer-implemented tools to help them locate the desiredinformation. One of the most commonly and widely usedcomputer-implemented tools is a search tool, also referred to as asearch engine. To search for information relating to a specific topic onthe Internet, a user typically provides a few words, often referred toas a “search query” or simply “query”, describing the topic to a searchtool. The search tool conducts a search based on the search query usingvarious search algorithms and generates a set of search results, eachcorresponding to some information that is most likely to be related tothe search query. The search results are then presented to the user.

Sophisticated search tools implement many other functionalities inaddition to merely identifying the search results as a part of thesearch process. For example, a search tool usually ranks the identifiedsearch results according to their relative degrees of relevance withrespect to the search query, such that the search results that arerelatively more relevant to the search query are ranked higher than andconsequently are presented to the network user before the search resultsthat are relatively less relevant to the search query. Sometimes, asearch result may be associated with a clickable call-to-action icon orbutton so that the user may click on it to further interact with thesearch result. There are continuous efforts to improve the quality ofthe search results generated by the search tools.

SUMMARY OF PARTICULAR EMBODIMENTS

This disclosure generally relates to improving search experiences forInternet users. More specifically, in particular embodiments, when auser accesses a user interface provided by a computer-implemented searchtool in order to conduct a search, before the user enters anything(e.g., any search query) into the input field included in the userinterface, a set of search results is compiled and presented to the user(e.g., as suggestions). Thereafter, the user has the choice of: (1)interacting with any of the suggested search results; or (2) entering asearch query, which causes another set of search results to be compiledin response to the search query and presented to the user.

In particular embodiments, in response to a user accessing a search tooland before the user submitting any search query or portion thereof tothe search tool, compiling a first set of search results based oninformation known about the user and presenting the first set of searchresults to the user.

These and other features, aspects, and advantages of the disclosure aredescribed in more detail below in the detailed description and inconjunction with the following figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example graph that represents the informationcontained in a social-networking system.

FIG. 2A illustrates an example user interface through which a user maysearch for information.

FIGS. 2B-2C illustrates an example user interface where a search resultis associated with various call-to-action elements.

FIG. 3 illustrates an example method for constructing a set of searchresults in response to a search query.

FIG. 4 illustrates an example method for suggesting a set of searchresults to a user before the user has provided any search query orportion thereof.

FIG. 5 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

This disclosure is now described in detail with reference to a fewembodiments thereof as illustrated in the accompanying drawings. In thefollowing description, numerous specific details are set forth in orderto provide a thorough understanding of this disclosure. However, thisdisclosure may be practiced without some or all of these specificdetails. In other instances, well known process steps and/or structureshave not been described in detail in order not to unnecessarily obscurethis disclosure. In addition, while the disclosure is described inconjunction with the particular embodiments, it should be understoodthat this description is not intended to limit the disclosure to thedescribed embodiments. To the contrary, the description is intended tocover alternatives, modifications, and equivalents as may be includedwithin the spirit and scope of the disclosure as defined by the appendedclaims.

A computer-implemented search tool is designed to search for informationrelevant to specific topics on one or more networks, such as theInternet or an Intranet. To conduct a search, a network user may issue asearch query to the search tool. The search query generally contains oneor more words that describe a topic. In response, the search tool mayidentify a set of search results, each corresponding to some informationthat is likely to be related to the search query. The set of searchresults may be ranked based on any number of factors and presented tothe user according to their respective ranks.

When ranking a set of search results with respect to a search query,many different factors may be considered. For example, the content ofeach search result may be analyzed to determine its degree of relevanceto the search query. In addition, particular embodiments may rank thesearch results based on factors such as, for example and withoutlimitation, the context in which the search is being conducted, thenature or characteristics of the topic described by the search query,the time when and the location where the search is requested, and thesocial-networking information and the behavior profile of the userrequesting the search.

Particular embodiments may also boost the ranks of certain searchresults according to objectives such as, for example and withoutlimitation, the business strategies or goals of the entity providing thesearch tool. In particular embodiments, the search tool may be providedby or associated with a social-networking system. The social-networkingsystem may have any number of features, such as, for example and withoutlimitation, enabling its users to contact their friends via emails ormessages, organize events, form social groups, upload and share photos,read news feeds, use various web-based applications (e.g., providedeither by the social-networking system or third parties), play onlinegames, and so on. In particular embodiments, one or more search resultsin the set may be associated with such a feature. When ranking the setof search results, the level of interaction the user has with thefeature may be taken into consideration. In some cases, the respectiveranks of the search results associated with the feature may be boosted(i.e., artificially increased) in order to bring the feature to theuser's attention in the hopes that the level of interaction the user haswith the feature may be increased.

In particular embodiments, one or more search results in the set may beassociated with various call-to-action elements. The term “call toaction”, in the context of network content and more specifically, in thecontext of user experience, is a user-interface element that prompts anaction from a user to initiate some function or process, such asinitiating a call or other communication. The most popular manifestationof call to action in web-based interfaces is in the form of clickableicons or buttons, which, when clicked, perform specific actions or leadto specific network content. In particular embodiments, a search resultmay be associated with a call-to-action element (e.g., an icon orbutton) so that a user may click on it to further interact with thesearch result. Sometimes, multiple types of call-to-action elements maybe appropriately associated with a specific search result. Particularembodiments may select one of the call-to-action elements for the searchresult based on various factors, such as, for example and withoutlimitation, the context in which the search is being conducted, thenature or characteristics of the topic described by the search query,the content of the search result, the time when and the location wherethe search is requested, the social-networking information and thebehavior profile of the user requesting the search, and the businessstrategies or objectives of the entity (i.e., the social-networkingsystem) providing the search tool.

In particular embodiments, one or more search results may be suggestedto a user as soon as the user accesses the search tool and before theuser has entered any search query or portion thereof. These searchresults may be compiled and/or ranked based on a number of factors, suchas, for example and without limitation, the fact that the user hasaccessed the search tool indicates that the user intends to search forsome information, the identity of the user, the profile and socialinformation about the user, behavior patterns of the user, past actionstaken by the user, the time when the user accesses the search tool, thelocation of the user where the user accesses the search tool, etc. Inparticular embodiments, these may be used to construct a model for theuser, which may then be used to determine how the suggested searchresults may be compiled and ranked for the user each time the useraccesses the search tool.

A social network, in general, is a social structure made up of entities,such as individuals or organizations, that are connected by one or moretypes of interdependency or relationships, such as friendship, kinship,common interest, financial exchange, dislike, or relationships ofbeliefs, knowledge, or prestige. In more recent years, social networkshave taken advantage of the Internet. There are social-networkingsystems existing on the Internet in the form of social-networkingwebsites. Such social-networking websites enable their members, who arecommonly referred to as website users, to perform various socialactivities. For example, the social-networking website operated byFacebook, Inc. at www.facebook.com enables its users to communicate withtheir friends via emails, instant messages, or blog postings, organizesocial events, share photos, receive news of their friends orinteresting events, play games, organize events, etc.

A social-networking system may contain a vast amount of informationrelated to its users. Such information is not limited to the socialconnections of the individual users, but may include, for example andwithout limitation, demographical information, network or socialactivities, behavior profiles, and personal preferences, interests, orhobbies of the individual users. Particular embodiments may representthe information contained in a social-networking system using a graphthat may have any number of nodes and edges, an example of which isillustrated in FIG. 1.

In graph 100 illustrated in FIG. 1, each node may represent an entity,which may be human (e.g., user of the social-networking system) ornon-human (e.g., location, event, action, business, object, message,post, image, web page, news feed, etc.). Two nodes are connected with anedge if the two nodes are related in some way (i.e., there is arelationship between the two nodes). Example cases when two nodes ingraph 100 may be related and thus connected with an edge may include,without limitation, (1) the two nodes represent two users of asocial-networking system respectively, and the two users are sociallyconnected (e.g., friends of each other); (2) the two nodes represent auser of the social-networking system and an event respectively, and theuser has attended the event; (3) the two nodes represent a user of thesocial-networking system and a location, and the user has been to thelocation; (4) the two nodes represent a user of the social-networkingsystem and the user has interacted with (e.g., viewed) the web page; (5)the two nodes represent an event and a location respectively, and theevent is held at the location; (6) the two nodes represent a user of thesocial-networking system and an image (e.g., a digital photograph)respectively, and the user is in the image; (7) the two nodes representa user of the social-networking system and a product (e.g., a mobiletelephone) respectively, and the user owns and uses the product; and (8)the two nodes represent a user of the social-networking system and asoftware application (e.g., a web-based game) respectively, and the useruses the application (e.g., plays the game). A connection may existbetween two humans, a human and a non-human entity, and two non-humanentities. Any type of relationship between two human or non-humanentities may result in a connection between the two entities.

In graph 100, when there is an edge between two specific nodes, the twonodes may be considered directly related. For example, edge 120Aconnects nodes 110A and 110B, and thus nodes 110A and 110B are directlyrelated. Similarly, edge 120B connects nodes 110B and 110C, and thusnodes 110B and 110C are directly related. When there is no edge betweentwo particular nodes, the two nodes may still be considered indirectlyrelated. For example, there is no edge directly connecting nodes 110Aand 110C; however, nodes 110A and 110C may still be consideredindirectly related through node 110B.

With respect to node 110A, node 110B has a closer relationship to itthan node 110C, because in graph 100, it takes one hop to go from node110A to node 110B, but it takes two hops to go from node 110A to node110C (i.e., through node 110B). In particular embodiments, with respectto two specific nodes, the less number of hops it takes to go from onenode to another node, the closer the two nodes are related.

In particular embodiments, a social-networking website implementing asocial-networking system may provide a search tool that enables itsusers to search for information relating to specific topics in thesocial context. The information represented by a graph, such as the oneillustrated in FIG. 1, may be used to help identify and rank the searchresults. FIG. 2A illustrates an example user interface 200 through whicha user may provide search queries and receive search results. In thiscase, there is an input field 210 through which a user may providesearch queries (e.g., search query 240), an icon 220 through which theuser may submit the search queries (e.g., by clicking on icon 220), andan output field 230 where the search results (e.g., search results242A-E) may be displayed. In addition, sometimes, a search result may beassociated with a call-to-action element. For example, in FIG. 2A,search result 242A is associated with call-to-action icon 244A; searchresult 242D is associated with call-to-action icon 244D; and searchresult 242E is associated with call-to-action icon 244E. Note that it isnot necessary that each and every search result in a set is associatedwith a call-to-action element. For example, in FIG. 2A, search results242B and 242C are not associated with any call-to-action elements.Call-to-action icons 244A, 244D, and 244E may be clickable icons. If auser clicks on one of these call-to-action icons, the user may furtherinteract with the associated search result or an action may be performedin connection with the associated search result. In particularembodiments, a call-to-action element may be presented in closeproximity to its associated search result.

In particular embodiments, multiple call-to-action elements may beassociated with the same search result. For example, if the searchresult is an email message, then the call-to-action elements associatedwith the email message search result may include “reply”, “forward”, and“delete”. If the search result is a restaurant, then the call-to-actionelements associated with the restaurant search result may include“check-in”, “like”, and “make reservation”. Some search results may bepersonally or professionally related to the user requesting the search(e.g., search results that are personal or professional contactsobtained from the user's address book). If the search result is a friendof the user's, the call-to-action elements may include “make phonecall”, “send email”, “send SMS”, “start chart session”, “post to wall”,etc. As these examples illustrate, what call-to-action elements aresuitable for a particular search result may depend on the nature of thesearch result itself.

In particular embodiments, each search result 242 may be associated withan expansion icon 252 (e.g., a downward triangle), as illustrated inFIG. 2B. When the user clicks on an expansion icon 252, thecall-to-action elements associated with the corresponding search result242 are displayed. Suppose that the user clicks on expansion icon 252B.In FIG. 2C, three call-to-action elements 244B associated with searchresult 242B are displayed. In addition, expansion icon 252B, associatedwith search result 242B, is replaced with a contraction icon 254B (e.g.,an upward triangle). When the user clicks on a contraction icon, thecall-to-action elements associated with the corresponding search result242 are hidden.

In particular embodiments, a user interface, such as the one illustratedin FIGS. 2A-2C, may be incorporated in a web page or a window panel fordisplay on the screen of an electronic device. For example, interface200 may be displayed on the screen of a mobile telephone or tablet.

In particular embodiments, the search tool may implement the “typeahead” feature, also known as “incremental search”, “incremental find”,or “real-time suggestions”, which is a user interface interaction methodto progressively search for and filter through texts (e.g., searchqueries). As a user types the text of a search query, one or morepossible matches for the text are found and immediately presented to theuser. This immediate feedback often allows the user to stop short oftyping the entire word or phrase of the search query. The user may alsochoose a closely related option from the presented list. In addition, inparticular embodiments, as the user types each character of a searchquery, a set of search results corresponding to the text thus far typedmay be presented to the user immediately. The search results may beupdated each time the user further types a character.

For example, suppose that a user wants to search for a person, andbegins to input the person's name as the search query. The user firsttypes the character “j” (e.g., in an input field included in a userinterface). At this point, some of the names that begin with the letter“j” (e.g., Jane, John, Joseph, Judith, etc.) may be suggested to theuser. In addition, a set of search results corresponding to one or moreof the suggested names (e.g., the first suggested name, Jane, or severalof the suggested names, Jane, John, Joseph, etc.) may be presented tothe user as well. Suppose that the user next types in the character “o”.At this point, some of the names that begin with the letters “jo” (e.g.,Joan, John, Jordon, Joseph, Joshua, etc.) may be suggested to the user.In addition, a set of search results corresponding to one or more of thesuggested names (e.g., Joan, or Joan, John, etc.) may be presented tothe user as well. This process may continue until the user finishestyping the name or selects one of the suggested names or search results.Type ahead is described in more detail in U.S. patent application Ser.No. 12/816,377, entitled “Search and Retrieval of Objects in a SocialNetworking System”, filed on 15 Jun. 2010, which is hereby incorporatedby reference in its entirety and for all purposes.

When typing search terms, the user can occasionally enter wrongcharacters as part of the search phrase. Using past knowledge of theparticular user's search queries, general user search queries, thelayout of the current keyboard on the device, and a definition of thelanguage or languages the user is likely to be typing in, allows thesearch provider to “guess” the likely search phrase even when anincorrect character has been entered. For example, the character “o” ona standard US QWERTY keyboard is most closely surrounded by thecharacters “o”, “p”, “;”, “,”, and “k”. If a user types “j”, then theset of results for all 2-letter combinations other than “jo” areunlikely to be the user's intended search query. If results exist for“jl”, they can be displayed. If no such results exist, then the resultsfor “jo” can be displayed to the user, with an indicator that anauto-correction has temporarily been applied. This reduces the need forthe user to correct the search query before tapping on the searchresult.

Note that in the example user interface 200 illustrated in FIGS. 2A-2C,an icon 220 is illustrated for submitting the search queries. This iconis not necessary in all cases. For example, when the “type ahead”feature is supported, icon 220 may not be needed and included in asearch-related user interface.

When a search tool receives a search query from a user, it may identifya set of search results considered relevant to the search query. Inaddition, the set of search results may be ranked based on variousfactors, objectives, and other considerations. The search results thatare ranked higher are presented to the user before the search resultsthat are ranked lower. Achieving a good ranking of the search resultsfor a user is especially desirable in some cases. For example, often, auser may conduct searches using a mobile electronic device (e.g., amobile telephone or a tablet computer) that has a relatively smalldisplay screen. Consequently, not many search results may be displayedand presented to the user within one screen. To avoid having to make theuser scroll down multiple screens to locate the specific search resultshe is looking for, if these search results are ranked higher andpresented to the user first, it can save the user time and make thesearch experience more efficient and pleasant for the user.

In some cases, certain search results may be associated with variouscall-to-action elements. Similarly, given a specific search result, itmay be desirable to select an appropriate call-to-action element (e.g.,icon or button) to be associated with the search result so that the usermay conveniently select the call-to-action element, which is oftenpresented in close proximity to the associated search result, to performan action with respect to the search result. Again, a user may conductsearches using a device (e.g., a mobile device) where it may not be easyand fast to navigate among the different user-interface elements. Toavoid having to make the user perform several interactions with thedevice in order to initiate the desired action with respect to a searchresult, if the appropriate call-to-action element is presented near theassociated search result, the user may easily select that call-to-actionelement to initiate the desired action with respect to a search result.

In particular embodiments, the user may be a user of thesocial-networking system providing or associated with the search tool.When compiling and ranking the set of search results in response to asearch query, the search tool may examine and analyze any relevantinformation available to the social-networking system and thus to thesearch tool (i.e., not just limited to web pages or other types ofcontents available on the Internet). For example and without limitation,the search tool may consider: (1) the nature and the context (especiallythe social context) of the search query (e.g., whether the search queryrefers to a person, a location, an event, a place, a software or webapplication, an object, etc.); (2) the social and demographicalinformation of the user requesting the search (e.g., the user's socialconnections, age, gender, family status, etc.); (3) the behavior profileor activities of the user (e.g., the user's daily routines, hobbies,interests, past network or social activities, etc.); (4) the time whenand the location where the user provides the search query; (5) whetherthe social-networking system desires to promote certain search results(e.g., search results associated with a feature of the social-networkingsystem, which the social-networking system desires to bring to theuser's attention); (6) whether the social-networking systems desires totrain or guide the user toward certain search results or behaviors; (7)the historical behavior of the user in using this or other search toolspreviously, such as whether the user typically selects or fails toselect certain classes of or certain particular results; (8) thehistorical behavior of the user in certain locations (e.g., the useralways selects results referring to people while at home, but selectsresults referring to businesses while not at home); and (9) thehistorical behavior of the user with respect to whether the userresponds to the search results after selecting one (e.g., by commentingor liking on the result that is delivered). In addition, in particularembodiments, the search tool may examine and analyze information storedon the electronic device used by the user to request the search, and mayinclude some of such information in the search results (e.g., if suchinformation is considered relevant to the search query).

FIG. 3 illustrates an example method of constructing a set of searchresults in response to a search query while taking into consideration atleast some of the social information relevant to the user requesting thesearch. As described above in connection with FIG. 1, the informationavailable in a social-networking system may be represented by a graph(e.g., graph 100 illustrated in FIG. 1). Such a graph may be used whencompiling and ranking a specific set of search results for a specificuser, as well as when selecting call-to-action elements for specificsearch results. In particular embodiments, since the user requesting thesearch is a user of the social-networking system, one of the nodes inthe graph associated with the social-networking system may represent theuser. By analyzing the graph, the search tool may determine what otherhuman and non-human entities are directly or indirectly related to theuser (e.g., other nodes in the graph that are connected, directly orindirectly, to the node representing the user), the types of theserelationships (e.g., what relationships the edges represent), and howclosely the other nodes are related to the node representing the user(e.g., the number of hops between each pair of nodes).

Particular embodiments may receive a search query from a user, asillustrated in STEP 310. The user may be a user of the social-networkingsystem and may provide the search query through an electronic device(e.g., a mobile telephone or tablet) connected to a network. Forexample, suppose that the search query is the word “Johnson”.

Particular embodiments may identify and compile a set of search resultsin response to the search query, as illustrated in STEP 320. The searchresults may include any type of information or content. For example, theword “Johnson” may refer to a person (e.g., Mary Johnson), a location(e.g., Johnson City), an establishment (e.g., Johnson's Hardware Store,or Johnson BBQ Restaurant), a brand or product name (e.g., Johnson &Johnson), a news story (e.g., a news feed about Jimmie Johnson), and amessage (e.g., a post by or concerning Tom Johnson). The search resultsmay be a mixture of many types of information considered relevant to thesearch query.

When selecting the search results, particular embodiments may obtaininformation from various sources, such as, for example and withoutlimitation, information stored locally on the electronic device used bythe user, information stored on servers or in databases associated withthe social-networking system, and information publically available onthe Internet. For example, the contact information of a person named“Mary Johnson” may be stored locally on the user's electronic device.Thus, the name and contact information (e.g., email address or phonenumber) of Mary Johnson may be selected as one of the search resultsincluded in the set. In this case, the search result comes from theinformation stored on the electronic device used by the user whenconducting the search. As another example, from the user's account atthe social-networking website, it may be determined that the user hasvisited a restaurant called “Johnson BBQ Restaurant” several times inthe past (e.g., the user has “checked in” with the social-networkingsystem when at Johnson BBQ Restaurant). Thus, the name and address ofJohnson BBQ Restaurant may be selected as one of the search resultsincluded in the set. In this case, the search result comes from theinformation stored on servers or in databases associated with thesocial-networking system. As another example, there may be news storieson the Internet about Jimmie Johnson participating in a NASCAR race.Thus, a web page containing such a news story may be selected as one ofthe search results included in the set. In this case, the search resultcomes from the information publically available on the Internet.

When the user submits the search query to initiate the search process,since the search results may be obtained from various sources, somespecific search results may become available before others. For example,search results obtained from information stored locally on theelectronic device used by the user may be readily available, beforesearch results obtained from information associated with thesocial-networking system or on the Internet, which may take a slightlylonger time to obtain since they are stored at remote sites. Inparticular embodiments, as soon as some search results become available,they may be presented to the user immediately, instead of having to waitfor other search results to become available. For example, in the abovescenario, the contact information for “Mary Johnson” is stored locallyon the user's electronic device and thus may be readily available. Uponthe user submitting the search query “Johnson”, the contact informationfor “Mary Johnson” may be presented to the user immediately. Then, asmore search results become available (e.g., obtained from thesocial-networking system or the Internet, such as the address and phonenumber of “Johnson BBQ Restaurant”), they may be added to the set ofsearch results compiled for the search query “Johnson” and presented tothe user accordingly (e.g., upon their availability).

A social-networking system may implement and support any number offeatures. In particular embodiments, one or more of the search resultsselected in response to the search query may be associated with one ormore of such features. In some cases, it may be possible that a searchresult not highly relevant to the search query may nevertheless beselected and included as one of the search results for the search query.For example, one feature may be that a user may play online gamesthrough the social-networking website. One of the available games called“Hatchlings” may be selected as one of the search results for searchquery “Johnson”, even though this game has little or no apparentrelevance to the word “Johnson”.

Particular embodiments may rank the set of search results based at leaston the social context or information relevant to the user requesting thesearch, as illustrated in STEP 330. Existing search engines (e.g.,search engines provided by Google, Microsoft, or Yahoo) rank searchresults based mainly on content relevance. They do not take intoconsideration of social context or information relevant to the specificuser requesting the search because they are not provided by anysocial-networking system that possesses such social information. Insteador in addition to content relevance, particular embodiments may takeinto consideration of the social information available to thesocial-networking system providing or associated with the search tool assome of the factors when ranking the search results for the user. In asense, the ranking is customized for each individual user based on theuser's social information. In addition, in particular embodiments, theuser's past behavior may be used to help identify and/or rank the searchresults.

For example, suppose that there are three people named “Johnson” foundas users of the social-networking system. Without any social informationof the user requesting the search, all three Johnsons may be similarlylikely to be the person the user is searching for. In one case, supposethat based on the social information of the user, one of the Johnsons(e.g., Johnson #1) may be a friend of the user while the other two arenot. In this case, Johnson #1 may be ranked higher than the other twoJohnsons as it is more likely that the user is searching for his friendinstead of some stranger. Furthermore, the other two Johnsons may noteven be selected as search results for the user. In another case,suppose that two Johnsons (e.g., Johnson #1 and Johnson #2) are bothfriends of the user, but Johnson #1 is directly connected to the user(e.g., in the graph, there is an edge between the two nodes representingthe user and Johnson #1 respectively) while Johnson #2 is indirectlyconnected to the user (e.g., connected through one or more other nodesbased on the graph), or the user has contacted Johnson #1 more times ormore frequently than Johnson #2. In this case, both Johnson #1 andJohnson #2 may be included in the set of search results, but Johnson #1may be ranked higher than Johnson #2.

As another example, suppose that there are two businesses having theword “Johnson” in their names: Johnson's Hardware Store and Johnson BBQRestaurant. The electronic device the user uses to conduct the searchmay supply location data (e.g., GPS data) that indicate the location ofthe device, and thus the location of the user, where the user requeststhe search on “Johnson”. In one case, suppose that Johnson BBQRestaurant is near the location of the user, while Johnson's HardwareStore is farther away. In this case, Johnson BBQ Restaurant may beranked higher than Johnson's Hardware Store as it is more likely thatthe user is searching for a nearby business. In another case, supposethat the user has visited Johnson BBQ Restaurant in the past but hasnever been to Johnson's Hardware Store. In this case, Johnson BBQRestaurant may also be ranked higher than Johnson's Hardware Store as itis more likely that the user is searching for a business with which hehas some contact in the past.

As a third example, suppose that there are two events having the word“Johnson” in their descriptions: Johnson's birthday party and JohnsonCity council meeting. If the user has been invited to Johnson's birthdayparty and has responded affirmatively to the invitation, then Johnson'sbirthday party may be ranked higher than Johnson City council meeting.If the user does not live in Johnson City, then Johnson City councilmeeting may not be included in the search results at all, as JohnsonCity council meeting probably has no relation to the user. If Johnson'sbirthday party is scheduled for Saturday evening at 6:00 pm and the timethe user requests the search is approximately Saturday at 5:00 pm, thenagain, Johnson's birthday party may be ranked higher, as it is morelikely that, as the time when the user conducts the search is fairlyclose to the time of the event, the user is checking for informationabout the event in preparation of attendance.

As a fourth example, suppose that based on the user's profile, the useris interested in auto racing, or the user has posted messages about autoracing. In one case, a news story (e.g., a news feed) about JimmieJohnson participating in a NASCAR race may be ranked higher as it ismore likely that the user may be interested in such a story. In anothercase, suppose that there are multiple news feeds about Jimmie Johnsonand auto racing, but some are older and some are newer. The more recentnews feeds that are closer to the time when the user conducts the searchmay be ranked higher than the older news feeds as they are more likelyto be of interest to the user than the older, and thus perhaps obsolete,news feeds.

As the above examples illustrate, when selecting and ranking a set ofsearch results in response to a search query submitted by a user, anyavailable information relevant to the user, to the search, or to thesearch query may be used. Such information may be stored at varioussites, such as on the user's device, in association with thesocial-networking system, or publicly on the Internet. If someinformation is already available on the user's device, then it may notbe necessary to obtain that information again from the social-networkingsystem or on the Internet. In particular embodiments, the informationcontained in the social-networking system may be represented as a graph(e.g., as illustrated in FIG. 1), and such a graph may be used tocompute, for each node in the graph, a coefficient that indicates itsrelationship to the node corresponding to the user requesting thesearch. This coefficient may be used to help rank the set of searchresults. In particular embodiments, each search result may correspond toa node in the graph as well.

In particular embodiments, a specific search result may be presented tothe user as soon as it becomes available. As more search results becomeavailable, they may be added to and co-mingled with the existing searchresults and presented to the user as well. At the same time, the searchresults may be ranked and the higher-ranked search results should bepresented to the user before the lower-ranked search results. However, afirst search result that becomes available before a second search resultis not necessarily ranked higher than the second search result. Inparticular embodiments, when ranking the search results, a ranking scoremay be computed for each search result, and the search results areranked based on their respective ranking scores. In particularembodiments, as each search result becomes available, its ranking scoremay be computed. Between two search results, suppose that the firstsearch result becomes available before the second search result;however, the ranking score of the first search result is lower than theranking score of the second result, which means that based on theirrespective ranking scores, the second search result should be presentedabove the first search result on the display screen of the user'selectronic device. By the time the second search result becomesavailable, the first search result may have already been presented tothe user (i.e., displayed on the display screen of the user's electronicdevice). In particular embodiments, since the second search result isranked higher than the first search result, when presenting the secondsearch result to the user, the second search result is placed (e.g.,inserted) above the first search result on the display screen of theuser's electronic device, effectively “pushing” the first search resultfurther down the display screen.

Optionally, particular embodiments may boost the respective ranks of oneor more search results in the set so that they may be presented to theuser before some other search results that would normally rank higherthan them. There are different reasons for boosting the rank of aspecific search result.

For example, the social-networking system providing the search tool maywish to achieve certain business objectives. Suppose that the userrequesting the search only has a very few friends (e.g., less than 5friends) identified in his account at the social-networking website. Thesocial-networking system may desire to encourage such a user to havemore friends. Suppose that the user is attending an event at a specificlocation when he conducts a search based on the query “Johnson”. In onecase, suppose that there are two Johnsons also attending the same event.Johnson #1 is the user's friend, while Johnson #2 is a stranger to theuser. Normally, Johnson #1 would be ranked higher than Johnson #2, as itis more likely that the user is searching for his friend who isattending the same event as he is. However, to encourage the user tomeet new friends, the rank of Johnson #2 may be booted to be above therank of Johnson #1, and so, Johnson #2 is presented to the user beforeJohnson #1. As a result, the user may meet and become friends withJohnson #2 as well. In another case, suppose that a person named“Smith”, who is a friend to the user's friend Johnson but is a strangerto the user, is also attending the same event. Normally, Smith may notbe included in the set of search results compiled for the query“Johnson”, or if Smith is included in the search results compiled forthe query “Johnson”, it may be ranked relatively low. However, toencourage the user to meet new friends, the rank of Smith may be boostedto be above the ranks of some other search results, and so is presentedto the user fairly soon. As a result, the user may meet and becomefriends with Smith, especially considering they share a common friend.

As a second example, the social-networking system may wish to train theuser about certain functionalities of features, or encourage certainuser behaviors. In one case, suppose that the user requesting the searchdoes not yet know he can play various online games through thesocial-networking website. When the user requests a search for the query“café”, an online game named “Café Mystery” may be included in thesearch results with a boosted rank. When this game is presented to theuser, the user may learn that he can play this game and many othergames. Such type of boosting may be repeated for a number of times,until the user is familiar with the functionalities or acquired certainbehavior patterns. Thereafter, it would no longer be necessary to boostthe ranks of similar types of search results.

As a third example, suppose that the social-networking system recentlyhas added a new feature to its website. To bring this new application toits users' attention, when a user requests a search, a search resultrelating to the new feature (e.g., a link pointing to the new feature)may be included as one of the search results. This may be determinedbased on whether a specific user has previously used the new feature. Inone case, if the user requesting the search has already used the newfeature, then the new feature may not need to be included in the searchresults, especially if the new feature has little or no relevance to thesearch query, as the user already knows about the new feature. On theother hand, if the user requesting the search has never interacted withthe new feature thus far, then the new feature may be selected as one ofthe search results. Furthermore, the ranking of the new feature may beboosted so that it is presented to the user fairly soon to capture theuser's attention.

In particular embodiments, given a set of search results compiled for asearch query provided by a user, a ranking score may be computed foreach search result in the set. In particular embodiments, the rankingscore may be computed using an algorithm that takes into considerationof many factors (e.g., various factors as inputs to the algorithm), suchas, for example and without limitation, the level of content relevanceof the search result to the search query, the level of social relevanceof the search result to the user requesting the search or the searchquery, the amount of boosting given to the search result, if any (e.g.,determined based on business objectives), the degree of closeness interms of time or location the search result has to the time when orlocation where the user requests the search, and so on. The algorithmmay combine all available factors (i.e., inputs) to determine a finalranking score for each search result. In particular embodiments, theranking score may be normalized to a number between 0 and 1.

In some cases, there may be one or more search results associated with afeature of the social-networking website included in the set. Given aspecific feature, in particular embodiments, if the user has notinteracted with the feature, then one or more search results associatedwith the feature may be included in the set. Furthermore, the ranking ofthese search results may be boosted (e.g., by providing a boostingfactor to the ranking algorithm that increases the respective rankingscores of these search results) so that these search results arepresented to the user sooner, in the hopes that the user may becomeaware of and subsequently use the feature. On the other hand, if theuser has already interacted with the feature, then no special action inconnection with the feature (e.g., selecting search results associatedwith the feature and boosting their ranking) may be needed.

Alternatively, in particular embodiments, given a specific feature, theamount or frequency of interaction the user has with the feature may bedetermined. For example, the level of interaction the user has with thefeature may be represented as a number between 0 and 1. If the user hasno interaction with the feature, the level of interaction may be 0. Onthe other hand, if the user uses the feature often, the level ofinteraction may be close to 1. When a user requests a search to beconducted on a given search query, whether to include one or more searchresults relating to the feature in the set of search results compiledfor the search query and if so, how much to boost the ranking of thesesearch results may depend on the current level of interaction the userhas with the feature. For example, if the user has no or very low levelof interaction with the feature, several search results relating to thefeature may be included in the set and their rankings may be boostedmuch higher. As the user interacts with the feature more and more, andnumber of search results relating to the feature included in the set maygradually decrease and their rankings may only need to be boostedslightly. Finally, as the user interacts with the feature frequently, nosearch result relating to the feature needs to be included in the set.

In particular embodiments, a boosting coefficient (e.g., a numberbetween 0 and 1) may be determined for each search result relating tothe feature based on the current level of interaction the user has withthe feature. This boosting coefficient may be supplied to the rankingalgorithm as one of the inputs when computing the ranking score for eachsearch result. For a search result not related to the feature, itsboosting coefficient may be set to 0 (i.e., no boosting). For example,when the user has no or little interaction with the feature, theboosting coefficient may be close to 1. As the user interacts with thefeature more and more, the boosting coefficient may gradually decreases.Finally, as the user interacts with the feature frequently, the boostingcoefficient may be close to 0.

In particular embodiments, when including search results relating to afeature in a set of search results compiled for a search query, the usermay be given an option to indicate that he is not interested in thatfeature. If the user has indicated that he is not interested in aspecific feature, then for subsequent searches requested by this user,search results relating to the specific feature are not artificiallyselected as search results, especially when such search results havelittle or no relevance otherwise to the search queries provided by theuser. Thus, once the user has indicated that he is not interested in aspecific feature, even if the user has never interacted with thefeature, search results relating to the feature are not artificiallyselected as search results, and when search results relating to thefeature are selected as search results, their respective ranks are notartificially boosted.

In some cases, the user's past behavior, when available, may be used tohelp select and/or rank the search results, as well as determine whichsearch results should have their ranks boosted. In particularembodiments, the user's behavior may be collected into a behaviorprofile, and the information contained in this profile may be used bythe search tool as needed. For example, suppose that a user rarely ornever selects (e.g., taps) on search results that are places. This maysuggest that the user is not interested in place-type search results. Inthis case, to take into consideration the user's personal preference,when selecting search results for the user, the search tool may includevery few or no place-type search results. Conversely, the system maywish to encourage the user to explore different types of search resultsin order to expand the user's interests. As a result, when selectingsearch results for the user, the search tool may include a larger numberof place-type search results. Furthermore, the ranks of at least some ofthe place-type search results may be artificially boosted to bring themto the user's attention. Similar strategies may be applied with respectto a specific search result (e.g., the user never selects a specificsearch result).

As another example, suppose that the user only selects specific types ofsearch results while at home, at work, or traveling. Thus, based on theuser's current location, a larger number of those specific types ofsearch results may be included, and if appropriate, the ranks of atleast some of those types of search results may be boosted.

As a third example, the user may like to view (e.g., read) web pagesassociated with another specific person (e.g., that person's personalpage or pages authored by that person, such as blogs), but due tovarious reasons, the user may not communicate directly to that person(e.g., reply or comment on the pages the user has read). Thesocial-networking system associated with the search tool may have abusiness object to encourage its users not only to passively viewinformation (e.g., content of web pages) but actively contributeinformation as well (e.g., by writing comments, replies, etc.). When theuser views a web page, it is more likely that the user may be willing tofurther interact with that page or with the author of the page, sincethe user has already shown interest in the content of the page. In thiscase, for example, the ranking of the web page may be boosted.

Particular embodiments may select call-to-action elements for specificsearch results in the set, as illustrated in STEP 340. In some cases,each and every search result in the set may have an associatedcall-to-action element, while in other cases, only specific searchresults in the set may have associated call-to-action elements. Whethera search result has an associated call-to-action element, and if so, thespecific type of call-to-action element may depend on many factors, andthis disclosure contemplates any applicable factors. For example andwithout limitation, the factors may include the user's historicalbehaviors, the time when the search result is presented to the user, thelocation of the user device (and thus the user) where the search resultis presented to the user, what the social-networking system associatedwith the search tool may encourage or desire the user to do with thesearch result, the information represented by the graph (e.g., graph 100illustrated in FIG. 1) associated with the social-networking system,etc.

There are many types of call-to-action elements, such as, for exampleand without limitation, sending an email, making a telephone call,responding to a message, posting a comment, liking a search result,executing a web-based application, purchasing a product, viewing thecontent of a search result, and so on. In fact, any action that may beperformed with respect to a search result may result in a correspondingcall-to-action element. Thus, this disclosure contemplates anyapplicable type of call-to-action element.

Sometimes, a search result may only be associated with one type ofsuitable or applicable call-to-action element. In such cases, that typeof call-to-action element is likely to be associated with the searchresult. However, more often, a search result may be associated withmultiple types of call-to-action elements. In such cases, one specifictype of call-to-action element may be selected for and associated withthe search result. What specific types of call-to-action elements aresuitable or applicable to a particular search result may depend on thenature or content of the search result itself.

Given a search result that may be associated with multiplecall-to-action elements, particular embodiments may select one or morecall-to-action elements that are considered most suitable or preferredfor the user at the time the search result, together with thecall-to-action element(s), is presented to the user. Note that differentcall-to-action elements may be considered suitable for different usersor at different times. In particular embodiments, for a given searchresult, a set of call-to-action elements suitable or applicable to thesearch result may be compiled based on available information. Thecall-to-action elements may then be ranked based on the various factors(e.g., the factors described above). In other words, to determine theappropriate call-to-action element(s) for a search result, particularembodiments may conduct a search (i.e., a second search forcall-to-action elements in response to the search result) to compile aset of suitable call-to-action elements for the search result, and thenrank the call-to-action elements in the set. The top-rankedcall-to-action element or elements may then be associated with thesearch result. In particular embodiments, the ranking of thecall-to-action elements may indicate the degrees of suitability of theindividual call-to-action elements to the search result, taking intoconsideration, for example, the user requesting the search, the timewhen and/or location where the search is request, the context of thesearch, etc.

In particular embodiments, when selecting and/or ranking thecall-to-action elements for the search results, the informationrepresented by the graph associated with the social-networking system(e.g., graph 100 illustrated in FIG. 1) may be used. As described above,since the user is a member of the social-networking system, one of thenodes in the graph may correspond to the user. In addition, a searchresult may also correspond to a specific node in the graph. Thus, therelationship or relationships between the user and the search result maybe determined by examining how the two nodes corresponding to the userand the search result, respectively, may be related (e.g., directly orindirectly) in the graph.

For example, suppose that a search result identified for the searchquery “Johnson” is “Mary Johnson”, who is a friend of the userrequesting the search. Further suppose that Mary Johnson has severalpieces of contact information available, including a home phone number,a work phone number, a mobile phone number, a fax number, an emailaddress, and an instant message address. Thus, the call-to-actionelements suitable for Mary Johnson may include “call home”, “call work”,“call mobile”, “send fax”, “send email”, and “send instant message”.These different call-to-action elements may be compiled into a set forMary Johnson. To rank the call-to-action elements in the set, the user'spast communication interactions with Mary Johnson may be analyzed. Ifthe user most frequently calls Mary Johnson's mobile phone whencontacting her, then the call-to-action element “call mobile” may beconsidered most suitable and selected to be associated with the searchresult “Mary Johnson”. On the other hand, if the user most frequentlysends Mary Johnson instant messages when contacting her, then thecall-to-action element “send instant message” may be considered mostsuitable and selected to be associated with the search result “MaryJohnson”. As this example illustrates, when ranking the set ofcall-to-action elements, the user's relationship with the search result“Mary Johnson” (i.e., social friends) as well as the user's pastbehavior pattern when interacting with the search result “Mary Johnson”may be relevant factors taking into consideration.

Alternatively, suppose that Mary Johnson is not a personal friend of theuser's but a celebrity figure (e.g., an actress) whom the user isinterested in. Because the relationship between the user and MaryJohnson is different from the above example, when Mary Johnson isidentified as a search result for the search query “Johnson”, thecall-to-action elements suitable for the search result “Mary Johnson”may also differ. In this example, the user may not have Mary Johnson'scontact information. Instead, the applicable call-to-action elements mayinclude “read headline news about Mary Johnson”, “buy tickets to MaryJohnson's latest movie”, “view Mary Johnson's photos”, “go to MaryJohnson's official website”, and “join Mary Johnson fan club”. If theuser has often read news stories about Mary Johnson, then thecall-to-action element “read headline news about Mary Johnson” may beconsidered the top-ranked call-to-action element and associated with thesearch result “Mary Johnson”. On the other hand, if there is a new moviefeaturing Mary Johnson released on the day when the user requests thesearch, then the “buy tickets” call-to-action element may be rankedhigher and associated with the search result “Mary Johnson”. In thiscase, the time when the user requests the search may affect the rankingof the call-to-action elements.

In some cases, the objectives of the social-networking system associatedwith the search tool may affect the selection and/or ranking of thecall-to-action elements. As a third example, suppose that thesocial-networking system wishes to promote a new web-based applicationsponsored by the social-networking system at its website. Thus, thisweb-based application may be included in the set of search resultsidentified for the search query. In addition, the ranking of theweb-based application may be boosted, as described above. Thecall-to-action element selected for and associated with the web-basedapplication may be “launch application” so that the user mayconveniently begin to use the web-based application by clicking on the“launch” call-to-action element displayed next to the web-basedapplication. As this example illustrates, a call-to-action element'sranking may be artificially boosted, in a manner similar to boosting theranking of a search result, as described above. In other words,sometimes, the provider of the search tool (e.g., the social-networkingsystem) may prefer specific call-to-action elements (e.g., based on itsbusiness objectives) and thus may choose to boost the ranks of itspreferred call-to-action elements over other call-to-action elements sothat these call-to-action elements preferred by the service provider maybe selected to be associated with the search results over the othercall-to-action elements.

In some cases, the time when the search is conducted and/or the locationwhere the user is when requesting the search may affect the selectionand/or ranking of the call-to-action elements. For example, suppose thata search result is “Johnson BBQ Restaurant”. Suitable call-to-actionelements that may be associated with Johnson BBQ Restaurant may include“check in”, “like”, and “make reservation”. If the user is currently atJohnson BBQ Restaurant (e.g., as indicated by the location data providedby the user's device) when the user requests the search, then “check in”may be selected to be associated with the search result, as it is likelythat the user is attending a dinner party at the restaurant and wishesto check in. Of course, multiple factors may together indicate whichspecific call-to-action element is more suitable for a given user at agiven time and/or location. Thus, suppose that based on the user'scalendar, it may be determined that: (1) there is a dinner party held atJohnson BBQ Restaurant; (2) the user has been invited to the dinnerparty (i.e., the user is an invitee); (3) the user has agreed to attendthe party (e.g., the user has RSVP'ed); and (4) the current time whenthe user requests the search is around the time of the dinner party. Allthese pieces of information may be combined to determine the ranks ofthe call-to-action elements for the search result.

As another example, suppose that the user is playing a computer game(e.g., web based or desktop based) with other people. Thus, when theweb-based game is a search result, the suitable call-to-action elementsthat may be associated with the game may include input actions (e.g.,designed by the game's developer) that the user may take with the game.Suppose that the game is an interactive game between multiple players(e.g., the players fight against each other). Based on the gamescenario, a player may kick or punch an opponent, dodge or block anattack from an opponent, etc. Thus, the call-to-action elements that maybe associated with the game may include “kick”, “punch”, “dodge”, and“block”. In particular embodiments, the call-to-action elementsassociated with the web-based game may ranked while taking intoconsideration the current state of the game with respect to the user.For example, if last action occurred with respect to the user in thegame is another player kicking the user, then the “block” call-to-actionelement may be ranked higher and associated with the game, as under thecircumstances, the user is likely to take a defensive action against theattack.

In particular embodiments, the call-to-action associated with a searchresult may be updated as the circumstances change. In the above example,once the user has clicked on the “block” call-to-action element to blockthe kick from the other player, the call-to-action element associatedwith the game may be updated to “kick” so that the user may now make acounter-attack move against the other player.

Different games may have different player actions. For example, a gamewhere players fight against vampires may support various attack moves oractions. Some of these attack actions may work well during daylightwhile others may work well after dark, since vampires are sensitive tolight. Thus, the current time when the search is requested may affectwhich call-to-action elements are associated with such a game. If thecurrent time is during the day, then one or more actions that work wellagainst the vampires during daylight may be selected to be associatedwith the game. On the other hand, if the current time is evening ornight, then one or more actions that work well against the vampiresafter dark may be selected to be associated with the game.

In particular embodiments, the available actions supported by a game maybe stored on the user's device or obtained from a server (e.g., a serverassociated with the social-networking system or the game developer). Forexample, the actions may be downloaded to the user's device if the userchooses to do so. Then, from time to time, as the game is updated andnew actions become available, they may be downloaded to the user'sdevice as needed.

As these examples suggest, by selecting a suitable or appropriatecall-to-action element for a search result, it may significantly improvethe user's interaction and search experience. For example, by placing a“buy tickets” call-to-action element next to a newly released movie, asdescribed in one of the above examples, the user may buy ticketsdirectly from the search results displayed on the user device. Thissaves the user the trouble of having to navigate to another website(e.g., a ticket selling website), search for the movie again, and thenbuy tickets. Similarly, by associating fighting moves with the web-basedgame, the user may play the game without having to actually go to thegame's website.

In particular embodiments, most, if not all, of the relevant informationmay be incorporated in the graph associated with the social-networkingsystem so that the information is readily available to the search toolfor selecting and ranking search results and call-to-action elements.Each information item, big or small, may correspond to a node in thegraph, and each relationship between two information item may correspondto an edge in the graph connecting the two corresponding nodes. Thus,the graph not only includes the information items but theirrelationships as well.

As explained above, sometimes, certain call-to-action elements may bepreferred by the provider of the search tool (e.g., thesocial-networking system), and thus, the ranks of these preferredcall-to-action elements may be boosted so that they may be selected overthe other call-to-action elements to be associated with the searchresults. In particular embodiments, when a search result is associatedwith a call-to-action element preferred by the social-networking system,the rank of this search result may also be boosted so that it ispresented to the user before other search results. This may increase thelikelihood that the user may select the preferred call-to-action elementassociated with the search result.

Particular embodiments may present the search results, according totheir respective ranks, to the user by display them on the user's devicein the order of their ranks, as illustrated in STEP 350.

Sophisticated user devices may have a sufficient amount of storage spacefor storing various types data. For example, with a smart telephone,some of the user's contacts, calendar appointments, events, task lists,instant messages, emails, news feeds, etc. may be stored on the device.Such information is available even when the device is offline (i.e.,disconnected from a network). In particular embodiments, the user mayconduct searches with his device regardless of whether the device isonline (i.e., connected to a network) or offline. If the device isonline when the user requests a search, the search results may becompiled based on information both stored on the user's device as wellas information available with the social-networking system and on theInternet. If the device is offline when the user requests a search, thesearch results may be compiled based only on the information stored onthe user's device.

In particular embodiments, the search results are presented to the user(e.g., displayed on the screen of the user's electronic device) as soonas they become available. In addition, the search results may be ranked,and higher-ranked search results may be displayed on the screen of theuser's electronic device above the lower-ranked search results. When ahigher-ranked search result becomes available after a lower-rankedsearch result, it may be displayed on the screen of the user'selectronic device above the lower-ranked search result, which hasalready been displayed, by pushing the lower-ranked search resultfurther down on the screen of the user's electronic device.

In some cases, a user may start a search process (e.g., by submitting asearch query) while the user's device is offline. However, while thesearch is being conducted, the device may be connected to a network andcome online. In this case, before the device comes online, the searchresults may be obtained based on information stored locally on thedevice. After the device comes online, additional search results may beobtained from information available with the social-networking systemand on the Internet. The new search results, after they becomeavailable, may co-mingle with the existing search results. The rank ofeach search result may be determined. The presentation of the searchresults may be adjusted when necessary to incorporate the newlyavailable search results (e.g., inserting some newly available searchresults above some existing search results on the screen of the user'sdevice).

Sometimes, it may be difficult to describe certain types of searchresults (e.g., images, videos, etc.) with words. In addition, text entryon mobile hand-held devices can often be cumbersome and time-consuming.Consequently, it may be difficult to formulate sufficiently clear,precise, and descriptive search queries when a user wishes to search forsuch types of information. For example, there may be millions of imagesof the White House posted on the Internet. If a user wishes to findimages of the White House in a specific style (e.g., from a specificperspective or angle, in a specific setting, etc.), it may be difficultfor the user to formulate a search query, in words, to clearly andprecisely describe the type of images of the White House the user issearching for. On the other hand, if the user simply uses “white house”as the search query, too many images that are not the type of images theuser is looking for may be included as search results, and the user hasto filter through all of these, which may be very time consuming.

To further improve the search experiences for the users, particularembodiments may suggest a set of search results to a user as soon as theuser accesses the search tool and before the user has submitted anysearch query to the search tool. These suggested search results may becompiled and ranked based on known information about the user. FIG. 4illustrates an example method for suggesting a set of search results toa user before the user has provided any search query. Suppose that auser wishes to use a search tool (e.g., a search tool provided by asocial-networking system) to find some specific information, such as atelephone number, email address, or other contact information. The usermay access the search tool by invoking or executing the search tool on auser device (e.g., a mobile device such as a netbook computer or amobile telephone), as illustrated in STEP 410. Typically, the searchtool provides a user interface, such as user interface 200 illustratedin FIGS. 2A-2C, which may include input field 210 that enables the userto enter and submit search queries and output field 230 for displayingthe search results. Executing the search tool on the user device maycause the user interface provided by the search tool to be displayed onthe user device.

In particular embodiments, before the user has entered and submitted anysearch query to the search tool (e.g., via input field 210), and infact, before the user has typed a single character in input field 210, aset of search results may be compiled for the user based on informationalready known about the user, as illustrated in STEP 420. In particularembodiments, some of the search results may be associated withappropriate call-to-action elements, which may be selected as describedabove. As discussed above, the search process may access and presentlocally stored results prior to or concurrently with obtaining searchresults from one or more remote sources.

At the time when the user accesses the search tool, there are varioustypes of information already known about the user. For example, the factthat the user has accessed a search tool inherently suggests that theuser is looking for some information. Thus, it is an appropriateresponse to suggest some search results in which the user may beinterested to the user. From the user device used by the user, the timewhen the user accesses the search tool may be determined. In addition,the location of the user device at the time the user accesses the searchtool, which means the location of the user, may be determined. Forexample, if the user device is a mobile telephone, the wireless signalof the telephone may be used to determine the location of the telephone(e.g., by triangulating the signal), and thus of the user. If the userdevice has a location sensor (e.g., GPS sensor), the sensor data may beused to determine the location of the device, and thus of the user.

Moreover, the identity of the user may be determined (e.g., from theidentity of the user device). Consequently, various information aboutthe user may be accessed and analyzed. For example, if the user is amember of the social-networking website, there may be information aboutthe user stored with the social-networking website (e.g., in the user'sprofile or account settings), such as, for example and withoutlimitation, the user's demographical information, social connections(e.g., families, friends, co-workers, etc.), social calendar and events,hobbies and interests, and so on. In addition, past actions performed bythe user (e.g., actions performed in connection with the user device)may be logged and stored, and each action may be associated with atimestamp indicating when that action has been performed and otherrelevant information. In particular embodiments, these historicalactions may be compiled to construct a behavior pattern for the user.For example, the behavior pattern may indicate that the user usuallydines out on Thursday evenings, calls his parents on Saturday mornings,takes his child to see movies at a local theatre on Sunday afternoons,and so on. With some implementations, the social-networking website maystore all the available information in a graph, such as the oneillustrated in FIG. 1. Note that the social-networking website may havemany members and the information about all the members may be compiledand stored in the graph.

In particular embodiments, the set of suggested search results may beranked based on various criteria. In particular embodiments, the rankingof the set of suggested search results, which has been compiled beforethe user has entered any search query, may differ from the ranking ofsets of search results compiled in response to search queries providedby the user. In the set of suggested search results, there may bespecific search results, such as photos or videos, that are difficult torank or discover using text keywords as search queries. In particularembodiments, the ranking of such search results may be increased whenthey are included in the set of suggested search results (i.e., the setof search results compiled without any search query). On the other hand,when such search results are included in the sets of search resultscompiled in response to the search queries, their ranking may berelatively lower (e.g., not artificially increased).

In particular embodiments, the known information about a user or usersof a social-networking website may be used to construct user models. Insome cases, a different model may be constructed for each individualuser. The model of a specific user may be constructed based on the knowninformation about that user (e.g., the user's profile, behavior pattern,social connections, etc.) and may reflect characteristics unique to thatuser. In other cases, a model may be constructed for a group of users(e.g., users belonging to a specific age group, users living in aspecific region, users working in a specific field, etc.). The model ofa group of users may be constructed based on the known information aboutall the users belonging to that group and may reflect characteristicscommon or typical to that group of users (e.g., users of ages between 20and 30 may prefer pop music or like action movies). Various techniquesmay be used to construct a model either for a specific user or a groupof users, including but not limited to: decision trees, expert systems,word clustering, statistical regressions, probability density,probabilistic automata and other machine learning techniques. Thesetechniques can be a combination of known information about the model andtrained information based on feedback from the user and/or usersapplicable to the model.

In particular embodiments, the known information about the user (e.g.,in the form of a user model of the user and/or a user model of a groupof users to which the user belongs) may be used to help select a set ofsearch results as suggestions to the user, without any search querybeing provided by the user. The inputs to the model can include sensordata from a mobile device (e.g., GPS data, cell tower data,accelerometer data, etc.), call logs, as well as the time of the accessand the identity of the user. For example, if the time the user accessesthe search tool is about 2:00 pm on a Sunday, the movies currentlyplaying at a local theater may be included in the set of search results,as it is possible that the user may wish to select a specific movie forhim and his child to attend. If the time the user accesses the searchtool is about 6:00 pm on a Thursday, and the location of the user deviceis in a downtown area, then restaurants within walking distance of theuser device may be included in the set of search results, as it ispossible that the user may wish to find a restaurant for dinner. If thetime the user accesses the search tool is about 10:30 am on a Saturday,the telephone number of the user's parents may be included in the set ofsearch results. In addition, the telephone number may be associated witha call-to-action element (e.g., represented as an icon displayed next tothe telephone number) to dial the number so that the user may call hisparents with a single action (e.g., tapping the call-to-action icon).

As another example, suppose that, according to the user's socialcalendar, the user is scheduled to attend an event soon, and the timethe user accesses the search tool is about 30 minutes before the startof the event. Information about the event (e.g., location, startingtime, driving direction from the user's current location to the locationof the event) may be included in the set of search results.

As another example, the user's profile may indicate that the user isvery interested in outdoor activities, such as hiking and bicycling.Alternatively, the user's past actions may indicate that the user oftenvisit websites related to outdoor activities. In this case, web pagesrelating to outdoor activities (e.g., information on hiking trails, bikeraces, etc.) or advertisements on products relevant to outdooractivities (e.g., hiking boots, backpacks, mountain bikes, etc.) may beincluded in the set of search results.

In particular embodiments, the set of search results may be ranked basedon, for example, the known information about the user, as illustrated inSTEP 430. With some implementations, the ranking algorithm used to rankthe search results obtained without any search query may differ from theranking algorithm used to rank the search results obtained in responseto a search query as described above. For example, as described above,when ranking search results identified in response to a search query,one factor to consider is the degrees of relevance the search resultshave with respect to the search query. However, when ranking searchresults compiled without any search query, the relevancy of the searchresults to the search query is unlikely to be a factor since there is nosearch query. Instead, the ranking algorithm may take other factors intoconsideration.

In particular embodiments, the past actions may provide recency dataabout the user's actions, which may indicate how recently the user hasperformed a specific action. As one example, suppose that there are anumber of images included in the set of search results compiled for theuser, and the recency data indicate that the user has just browsed manyimages posted online using a web browser (e.g., within the past 10minutes). In this case, the ranking algorithm may decrease the ranks ofthe images included in the set of search results, as it may be unlikelythat the user wishes to search for more images after having just viewedmany images. Conversely, the ranking algorithm may choose to increasethe ranks of the images included in the set of search results, as it maybe possible that the user has not found the images he is specificallylooking for, and thus has decided to search for them using the searchtool.

As another example, suppose that there are news stories from news feedsincluded in the set of search results. If the record of the user's pastaction indicates that the user has already read a specific news story,that news story may be excluded from the set of search results, or ifthe news story is still included in the set of search results, the rankof the news story may be lowered. If the user's behavior patternindicates that the user reads news feeds many times each day, the ranksof the news stories included in the set of search results, especiallythose that the user has not yet read, may be boosted.

As another example, suppose that the set of search results includes anumber of email messages the user has received recently. If the time theuser accesses the search tool is on a weekday during normal businesshours, then email messages sent by the user's co-workers may be rankedhigher than email messages sent by the user's friends. On the otherhand, if the time the user accesses the search tool is in the eveningsor on weekends, then email messages sent by the user's friends may beranked higher than email messages sent by the user's co-workers.

The search results may be presented to the user (e.g., displayed insideoutput field 230 on the user device) according to their respectiveranks, as illustrated in STEP 440. In some implementations, as soon asthe user accesses the search tool and when the user interface isinitially displayed on the user device, the output field included in theuser interface has already been populated with the suggested searchresults. In other words, when the user interface of the search tool isfirst presented to the user, it already includes the suggested searchresults compiled and ranked based on the known information about theuser. Thereafter, the user has the choice of interacting with any of thepresented search results (e.g., as the user normally interacts with asearch result identified in response to a search query) or enter asearch query in the input field included in the user interface (e.g.,when the user does not find the suggested search results useful).

If the user chooses to enter and submit a search query to the searchtool, the search tool may compile a new set of search results inresponse to the search query submitted, as described above in connectionwith FIG. 3. The new set of search results may be displayed in theoutput field included in the user interface, replacing the initial setof search results. With some implementations, with the “type ahead”feature supported, as soon as the user starts entering characters of thesearch query into the input field included in the user interface, thesearch tool may compile new search results, in response to thecharacters thus far entered, to replace the initial set of searchresults.

With some implementations, to provide a consistent search experience tothe user, each set of search results, whether it is compiled without asearch query (e.g., before the user has submitted any search query) orin response to a search query submitted by the user, is presented in thesame or similar format or layout so that multiple sets of search resultsall have the same or similar look and feel. The user does not need to beconcerned with how a set of search results is obtained, and instead, canconcentrate on locating the specific search results the user is lookingfor.

With some implementations, the search tool may take into account theuser's behavior using the interface prior to entering any search queryto compile a different set of search results once a search query hasbeen entered. For example, a user who scrolls down the list of suggestedsearch results prior to entering a search query has seen differentinformation from a user who immediately begins typing (e.g., withoutbothering to review any of the suggested search results). The searchtool may re-rank, restrict by category, or hide search results that theuser who scrolled down the list has seen.

The search tool functionalities (e.g., identifying, ranking, andboosting search results) described above may be implemented as a seriesof instructions stored on a computer-readable storage medium that, whenexecuted, cause a programmable processor to implement the operationsdescribed above. FIG. 5 illustrates an example computer system 500. Inparticular embodiments, one or more computer systems 500 perform one ormore steps of one or more methods described or illustrated herein. Inparticular embodiments, one or more computer systems 500 providefunctionality described or illustrated herein. In particularembodiments, software running on one or more computer systems 500performs one or more steps of one or more methods described orillustrated herein or provides functionality described or illustratedherein. Particular embodiments include one or more portions of one ormore computer systems 500.

This disclosure contemplates any suitable number of computer systems500. This disclosure contemplates computer system 500 taking anysuitable physical form. As example and not by way of limitation,computer system 500 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, or a combination of two or more ofthese. Where appropriate, computer system 500 may include one or morecomputer systems 500; be unitary or distributed; span multiplelocations; span multiple machines; or reside in a cloud, which mayinclude one or more cloud components in one or more networks. Whereappropriate, one or more computer systems 500 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 500 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 500 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 500 includes a processor 502,memory 504, storage 506, an input/output (I/O) interface 508, acommunication interface 510, and a bus 512. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 502 includes hardware for executinginstructions, such as those making up a computer program. As an exampleand not by way of limitation, to execute instructions, processor 502 mayretrieve (or fetch) the instructions from an internal register, aninternal cache, memory 504, or storage 506; decode and execute them; andthen write one or more results to an internal register, an internalcache, memory 504, or storage 506. In particular embodiments, processor502 may include one or more internal caches for data, instructions, oraddresses. This disclosure contemplates processor 502 including anysuitable number of any suitable internal caches, where appropriate. Asan example and not by way of limitation, processor 502 may include oneor more instruction caches, one or more data caches, and one or moretranslation lookaside buffers (TLBs). Instructions in the instructioncaches may be copies of instructions in memory 504 or storage 506, andthe instruction caches may speed up retrieval of those instructions byprocessor 502. Data in the data caches may be copies of data in memory504 or storage 506 for instructions executing at processor 502 tooperate on; the results of previous instructions executed at processor502 for access by subsequent instructions executing at processor 502 orfor writing to memory 504 or storage 506; or other suitable data. Thedata caches may speed up read or write operations by processor 502. TheTLBs may speed up virtual-address translation for processor 502. Inparticular embodiments, processor 502 may include one or more internalregisters for data, instructions, or addresses. This disclosurecontemplates processor 502 including any suitable number of any suitableinternal registers, where appropriate. Where appropriate, processor 502may include one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 502. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 504 includes main memory for storinginstructions for processor 502 to execute or data for processor 502 tooperate on. As an example and not by way of limitation, computer system500 may load instructions from storage 506 or another source (such as,for example, another computer system 500) to memory 504. Processor 502may then load the instructions from memory 504 to an internal registeror internal cache. To execute the instructions, processor 502 mayretrieve the instructions from the internal register or internal cacheand decode them. During or after execution of the instructions,processor 502 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor502 may then write one or more of those results to memory 504. Inparticular embodiments, processor 502 executes only instructions in oneor more internal registers or internal caches or in memory 504 (asopposed to storage 506 or elsewhere) and operates only on data in one ormore internal registers or internal caches or in memory 504 (as opposedto storage 506 or elsewhere). One or more memory buses (which may eachinclude an address bus and a data bus) may couple processor 502 tomemory 504. Bus 512 may include one or more memory buses, as describedbelow. In particular embodiments, one or more memory management units(MMUs) reside between processor 502 and memory 504 and facilitateaccesses to memory 504 requested by processor 502. In particularembodiments, memory 504 includes random access memory (RAM). This RAMmay be volatile memory, where appropriate. Where appropriate, this RAMmay be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 504 may include one ormore memories 504, where appropriate. Although this disclosure describesand illustrates particular memory, this disclosure contemplates anysuitable memory.

In particular embodiments, storage 506 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 506may include an HDD, a floppy disk drive, flash memory, an optical disc,a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB)drive or a combination of two or more of these. Storage 506 may includeremovable or non-removable (or fixed) media, where appropriate. Storage506 may be internal or external to computer system 500, whereappropriate. In particular embodiments, storage 506 is non-volatile,solid-state memory. In particular embodiments, storage 506 includesread-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 506 taking any suitable physicalform. Storage 506 may include one or more storage control unitsfacilitating communication between processor 502 and storage 506, whereappropriate. Where appropriate, storage 506 may include one or morestorages 506. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 508 includes hardware,software, or both providing one or more interfaces for communicationbetween computer system 500 and one or more I/O devices. Computer system500 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 500. As an example and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 508 for them. Where appropriate, I/O interface 508 mayinclude one or more device or software drivers enabling processor 502 todrive one or more of these I/O devices. I/O interface 508 may includeone or more I/O interfaces 508, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 510 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 500 and one or more other computer systems 500 or one ormore networks. As an example and not by way of limitation, communicationinterface 510 may include a network interface controller (NIC) ornetwork adapter for communicating with an Ethernet or other wire-basednetwork or a wireless NIC (WNIC) or wireless adapter for communicatingwith a wireless network, such as a WI-FI network. This disclosurecontemplates any suitable network and any suitable communicationinterface 510 for it. As an example and not by way of limitation,computer system 500 may communicate with an ad hoc network, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), or one or more portions of theInternet or a combination of two or more of these. One or more portionsof one or more of these networks may be wired or wireless. As anexample, computer system 500 may communicate with a wireless PAN (WPAN)(such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAXnetwork, a cellular telephone network (such as, for example, a GlobalSystem for Mobile Communications (GSM) network), or other suitablewireless network or a combination of two or more of these. Computersystem 500 may include any suitable communication interface 510 for anyof these networks, where appropriate. Communication interface 510 mayinclude one or more communication interfaces 510, where appropriate.Although this disclosure describes and illustrates a particularcommunication interface, this disclosure contemplates any suitablecommunication interface.

In particular embodiments, bus 512 includes hardware, software, or bothcoupling components of computer system 500 to each other. As an exampleand not by way of limitation, bus 512 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 512may include one or more buses 512, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, reference to a computer-readable storage medium encompasses oneor more non-transitory, tangible computer-readable storage mediapossessing structure. As an example and not by way of limitation, acomputer-readable storage medium may include a semiconductor-based orother integrated circuit (IC) (such, as for example, afield-programmable gate array (FPGA) or an application-specific IC(ASIC)), a hard disk, an HDD, a hybrid hard drive (HHD), an opticaldisc, an optical disc drive (ODD), a magneto-optical disc, amagneto-optical drive, a floppy disk, a floppy disk drive (FDD),magnetic tape, a holographic storage medium, a solid-state drive (SSD),a RAM-drive, a SECURE DIGITAL card, a SECURE DIGITAL drive, or anothersuitable computer-readable storage medium or a combination of two ormore of these, where appropriate. Herein, reference to acomputer-readable storage medium excludes any medium that is noteligible for patent protection under 35 U.S.C. §101. Herein, referenceto a computer-readable storage medium excludes transitory forms ofsignal transmission (such as a propagating electrical or electromagneticsignal per se) to the extent that they are not eligible for patentprotection under 35 U.S.C. §101. A computer-readable non-transitorystorage medium may be volatile, non-volatile, or a combination ofvolatile and non-volatile, where appropriate.

This disclosure contemplates one or more computer-readable storage mediaimplementing any suitable storage. In particular embodiments, acomputer-readable storage medium implements one or more portions ofprocessor 502 (such as, for example, one or more internal registers orcaches), one or more portions of memory 504, one or more portions ofstorage 506, or a combination of these, where appropriate. In particularembodiments, a computer-readable storage medium implements RAM or ROM.In particular embodiments, a computer-readable storage medium implementsvolatile or persistent memory. In particular embodiments, one or morecomputer-readable storage media embody software. Herein, reference tosoftware may encompass one or more applications, bytecode, one or morecomputer programs, one or more executables, one or more instructions,logic, machine code, one or more scripts, or source code, and viceversa, where appropriate. In particular embodiments, software includesone or more application programming interfaces (APIs). This disclosurecontemplates any suitable software written or otherwise expressed in anysuitable programming language or combination of programming languages.In particular embodiments, software is expressed as source code orobject code. In particular embodiments, software is expressed in ahigher-level programming language, such as, for example, C, Perl, or asuitable extension thereof. In particular embodiments, software isexpressed in a lower-level programming language, such as assemblylanguage (or machine code). In particular embodiments, software isexpressed in JAVA, C, or C++. In particular embodiments, software isexpressed in Hyper Text Markup Language (HTML), Extensible MarkupLanguage (XML), or other suitable markup language.

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

This disclosure encompasses all changes, substitutions, variations,alterations, and modifications to the example embodiments herein that aperson having ordinary skill in the art would comprehend. Similarly,where appropriate, the appended claims encompass all changes,substitutions, variations, alterations, and modifications to the exampleembodiments herein that a person having ordinary skill in the art wouldcomprehend. Moreover, reference in the appended claims to an apparatusor system or a component of an apparatus or system being adapted to,arranged to, capable of, configured to, enabled to, operable to, oroperative to perform a particular function encompasses that apparatus,system, component, whether or not it or that particular function isactivated, turned on, or unlocked, as long as that apparatus, system, orcomponent is so adapted, arranged, capable, configured, enabled,operable, or operative.

What is claimed is:
 1. A method comprising, by one or more processorsassociated with a social-networking system: compiling, by the one ormore processors, a set of search results based on information knownabout a user stored by the social-networking system, the search resultsbeing compiled before the user inputs any search query or portionthereof, each search result being associated with one or morecall-to-action elements applicable to the search result, eachcall-to-action element prompting an action from the user related to thesearch result via the social-networking system; and sending, to a clientdevice of the user, the set of search results with the call-to-actionelements for presentation to the user, wherein the call-to-actionelements are presented to the user in proximity to their associatedsearch results.
 2. The method of claim 1, further comprising: accessinga social graph comprising a plurality of nodes and a plurality of edgesconnecting the nodes, each of the edges between two of the nodesrepresenting a single degree of separation between them, the nodescomprising: a first node corresponding to the user; and a plurality ofsecond nodes that each correspond to a search result of the set ofsearch results.
 3. The method of claim 1, wherein sending the set ofsearch results comprises: associating, for each search result, at leastone top-ranked call-to-action element with the search result.
 4. Themethod of claim 1, wherein one of the call-to-action elementscorresponds to an action that is likely to be performed by the user withrespect to the search result.
 5. The method of claim 1, wherein one ofthe call-to-action elements corresponds to an action that is convenientfor the user to perform with respect to the search result.
 6. The methodof claim 1, wherein one of the call-to-action elements corresponds to anaction that the social-networking system encourages the user to performwith respect to the search result.
 7. The method of claim 1, wherein oneof the call-to-action elements corresponds to a search querycorresponding to the search result, wherein the call-to-action elementis selectable by the user to execute a search query.
 8. The method ofclaim 7, further comprising: receiving, from the client device, aselection of a call-to-action element corresponding to a search query;identifying one or more content items of the online social network thatmatch the search query; and sending, to the client device of the firstuser in response to the selection of the call-to-action element, one ormore search results corresponding to one or more of the identifiedcontent items, respectively.
 9. The method of claim 1, furthercomprising: receiving, from the client device of the user, an indicationof the user accessing a search tool at the client device of the user,the indication being received before the user inputs any search query orportion thereof to the search tool.
 10. The method of claim 1, whereinthe information known about the user comprises the user's demographicaldata, social connections, interests, hobbies, past actions, behaviorpatterns, and calendar entries.
 11. The method of claim 1, wherein theset of search results is compiled further based on a time when the useraccesses a search tool at the client device of the user.
 12. The methodof claim 1, wherein the set of search results is compiled further basedon a location where the user accesses a search tool at the client deviceof the user.
 13. The method of claim 1, further comprising ranking theset of search results based on the information known about the user. 14.The method of claim 1, further comprising, for each search result of theset of search results: identifying the one or more call-to-actionelements applicable to each search result of the set of search resultsbased on the information known about the user; ranking thecall-to-action elements based a suitability of the call-to-actionelement to the search result and further based on at least onerelationship in an online social network between the user and anotheruser associated with the search result; and presenting the one or morecall-to-action elements as ranked with the applicable search result. 15.The method of claim 1, further comprising: receiving a search querysubmitted by the user to a search tool at the client device of the user;identifying a new set of search results in response to the search query;and presenting the new set of search results to the user, replacing theset of search results.
 16. One or more computer-readable non-transitorystorage media embodying software that is operable when executed to:compile a set of search results based on information known about a userstored by a social-networking system, the search results being compiledbefore the user inputs any search query or portion thereof, each searchresult being associated with one or more call-to-action elementsapplicable to the search result, each call-to-action element promptingan action from the user related to the search result via thesocial-networking system; and send, to a client device of the user, theset of search results with the call-to-action elements for presentationto the user, wherein the call-to-action elements are presented to theuser in proximity to their associated search results.
 17. Asocial-networking system comprising: one or more processors; and anon-transitory memory coupled to the processors comprising instructionsexecutable by the processors, the processors operable when executing theinstructions to: compile a set of search results based on informationknown about a user stored by the social-networking system, the searchresults being compiled before the user inputs any search query orportion thereof, each search result being associated with one or morecall-to-action elements applicable to the search result, eachcall-to-action element prompting an action from the user related to thesearch result via the social-networking system; and send, to a clientdevice of the user, the set of search results with the call-to-actionelements for presentation to the user, wherein the call-to-actionelements are presented to the user in proximity to their associatedsearch results.